https://scholar.dgist.ac.kr/handle/20.500.11750/10144 Sun, 23 Aug 2026 22:31:27 GMT 2026-08-23T22:31:27Z https://scholar.dgist.ac.kr/handle/20.500.11750/60621 Title: Low-Complexity Single-Chain ISAC Receiver via Beat-Signal Domain FRFT Author(s): Kim, Bong-Seok; Lee, Jonghun; Kim, Sangdong Abstract: This letter introduces a single-chain Fractional Fourier Transform (FRFT)-based receiver for integrated sensing and communication (ISAC) that eliminates the need for matched-filter banks. While Slope-Shift Keying (SSK) is a promising modulation technique for automotive FMCW radar, conventional coherent receivers require a bank of matched filters, making the receiver processing complexity scale linearly with the modulation order. To overcome this limitation, we apply the FRFT in the beat-signal domain. By exploiting the chirp-rate-dependent energy focusing property, this approach converts slope discrimination into a fractional-domain peak detection problem. Consequently, it enables efficient high-order symbol detection using only a single standard receive processing chain. Simulation results demonstrate that the proposed scheme reduces the modulation-order-dependent digital processing burden while maintaining stable bit error rate (BER) performance and radar sensing performance under high-mobility Rician multipath V2V conditions. Wed, 31 Dec 2025 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/60621 2025-12-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60554 Title: Attention-Based Multimodal Transformer-LSTM Fusion Networks for Enhanced Lithium-ion Battery State-of-Charge Prediction with Aging Pattern Consideration Author(s): HyunKi, Ryu; Cho, SungRae; Jon, YongJun; Kim, Bonghwan; Kim, Dongkyun Abstract: Lithium-ion batteries play a pivotal role in electric vehicles (EVs) and energy storage systems, where accurate State-of-Charge (SoC) prediction is essential for ensuring the efficiency and safety of battery management systems (BMS). This study conducts an in-depth analysis of the temperature factors that most significantly influence battery remaining capacity prediction, with a particular focus on accurately predicting battery SoC variations under extreme temperature conditions ranging from - 30 degrees C to 80 degrees C. The research methodology employs a multimodal neural network that com-bines Transformer architecture, which demonstrates superior performance in processing static characteristic data, with Long Short-Term Memory (LSTM) networks, which exhibit exceptional capabilities in time-series data processing. The model effectively integrates battery temperature performance data with NASA aging datasets through an attention-based fusion approach, enabling efficient information exchange between heterogeneous data modalities. Experimental results demonstrate that the proposed model achieves an MAE of 0.2438 +/- 0.016 on the overall test set across - 30 degrees C to 80 degrees C. Additionally, the model attained an RMSE of 0.3156 +/- 0.020 and an R & sup2; of 0.9501 +/- 0.009, representing a 24.6% improve-ment over baseline LSTM models, and an additional 14% improvement attributable to the attention mechanism when compared to simple concatenation-based fusion. Furthermore, the attention-based fusion approach demonstrates marked performance improvements compared to simple combination methods. These results demonstrate potential applicability to electric vehicles and energy storage systems through comprehensive laboratory validation under diverse temperature conditions (-30 degrees C to 80 degrees C), though field testing is required for deployment verification. Thu, 30 Apr 2026 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/60554 2026-04-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60466 Title: Multimodal Remote Heart Rate Estimation via Spatio-Temporal Transformers and Adaptive Fusion Author(s): Kim, Hyunduk; Lee, Sang-Heon; Sohn, Myoung-Kyu; Kim, Junkwang; Park, Hyeyoung Abstract: Remote photoplethysmography (rPPG) enables noncontact heart rate (HR) estimation from facial videos. Despite recent advances, single-modality methods remain vulnerable to motion, illumination changes, and modality-specific degradations. We address these limitations with a multimodal framework that explicitly leverages complementary RGB and infrared (IR, thermal or NIR) streams. Built on a 3-D SwiftFormer backbone, the method integrates three modules: 1) a context-aware temporal difference convolution (CTDC) that amplifies motion-sensitive cues via multiscale temporal differencing; 2) a bidirectional cross-attention (BCA) that enables hierarchical information exchange between modalities; and 3) a cross-modal gating fusion (CMGF) that adaptively combines features using a temperature-scaled logit-difference gate. Training is guided by a hybrid objective over time and frequency, augmented with a scheduled soft-DTW alignment term. Extensive experiments on two public datasets demonstrate consistent improvements over state-of-the-art baselines, with ablation studies confirming the contributions of CTDC, BCA, CMGF, and soft-DTW. These results highlight the effectiveness of explicit cross-modal interaction and adaptive fusion for robust, accurate remote HR (rHR) estimation. Sat, 31 Jan 2026 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/60466 2026-01-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60266 Title: Performance Analysis of Sensor Fusion Models Using Unmanned Ground Vehicle Author(s): Minsu-Jo; Baek, Youngmi; Lee, Jin-Hee; Son, Sang Hyuk Abstract: In this paper, we analyze the performance of various sensor fusion models using an unmanned ground vehicle. In the given attack scenarios, we examine how the attacks influence on each fusion model by comparing the results of the models. We conduct the experiments with real measurement data obtained from an unmanned ground vehicle. © 2016 IEEE. Thu, 31 Dec 2015 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/60266 2015-12-31T15:00:00Z